scholarly journals A Two-Layer Network Dynamic Congestion Pricing Based on Macroscopic Fundamental Diagram

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Bangyang Wei ◽  
Daniel(Jian) Sun
Keyword(s):  
Travel Time ◽  
Field Experiments ◽  
Traffic Assignment ◽  
Congestion Pricing ◽  
Large Network ◽  
Mean Flow ◽  
Fundamental Diagram ◽  
Macroscopic Fundamental Diagram ◽  
Dynamic Congestion Pricing ◽  
Total Travel Time

Dynamic congestion pricing has attracted increasing attentions during the recent years. Nevertheless, limited research has been conducted to address the dynamic tolling scheme at the network level, such as to cooperatively manage two alternative networks with heterogeneous properties, e.g., the two-layer network consisting of both expressway and arterial network in the urban areas. Recently, the macroscopic fundamental diagram (MFD) developed by both field experiments and simulation tests illustrates a unimodal low-scatter relationship between the mean flow and density network widely, providing the network traffic state is roughly homogeneous. It reveals traffic flow properties at an aggregated level and sheds light on dynamic traffic management of a large network. This paper proposes a bilevel programming toll model, incorporating MFD to solve the unbalanced flow distribution problem within the two-layer transportation networks. The upper level model aims at minimizing the total travel time, while the lower level focuses on the MFD-based traffic assignment, which extends the link-based traffic assignment to network wide level. Genetic algorithm (GA) and the method of successive average were adopted for solving the proposed model, on which an online experimental platform was established using VISSIM, MATLAB, and Visual Studio software packages. The results of numerical studies demonstrate that the total travel time is decreased by imposing the dynamic toll, while the total travel time savings significantly outweigh the toll paid. Consequently, the proposed dynamic toll scheme is believed to be effective from both traffic and economic points of view.

scholarly journals Feedback Gating Control for Network Based on Macroscopic Fundamental Diagram

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
YangBeibei Ji ◽  
Chao Mo ◽  
Wanjing Ma ◽  
Dabin Liao
Keyword(s):  
Control Policy ◽  
Fixed Time ◽  
Control Model ◽  
Urban Traffic ◽  
Network Congestion ◽  
Mean Flow ◽  
Fundamental Diagram ◽  
Macroscopic Fundamental Diagram ◽  
External Demand ◽  
Demand And Capacity

Empirical data from Yokohama, Japan, showed that a macroscopic fundamental diagram (MFD) of urban traffic provides for different network regions a unimodal low-scatter relationship between network vehicle density and network space-mean flow. This provides new tools for network congestion control. Based on MFD, this paper proposed a feedback gating control policy which can be used to mitigate network congestion by adjusting signal timings of gating intersections. The objective of the feedback gating control model is to maximize the outflow and distribute the allowed inflows properly according to external demand and capacity of each gating intersection. An example network is used to test the performance of proposed feedback gating control model. Two types of background signalization types for the intersections within the test network, fixed-time and actuated control, are considered. The results of extensive simulation validate that the proposed feedback gating control model can get a Pareto improvement since the performance of both gating intersections and the whole network can be improved significantly especially under heavy demand situations. The inflows and outflows can be improved to a higher level, and the delay and queue length at all gating intersections are decreased dramatically.

Incorporating Travel Time Reliability in Equitable Congestion Pricing Schemes for Heterogeneous Users and Bimodal Networks

2021 ◽  
pp. 036119812110197
Author(s):  
Fatemeh Fakhrmoosavi ◽  
Ali Zockaie ◽  
Khaled Abdelghany
Keyword(s):  
Travel Time ◽  
Optimization Algorithm ◽  
Travel Demand ◽  
Demand Management ◽  
Congestion Pricing ◽  
User Equilibrium ◽  
Travel Time Reliability ◽  
Travel Demand Management ◽  
Equity Issues ◽  
Total Travel Time

Congestion pricing is proposed as an effective travel demand management strategy to circumvent the problem of congestion and generate revenue to finance developmental projects. There are several studies focusing on optimal pricing strategies to minimize the congestion level or maximize the revenue of the system. However, with regard to equity issues, benefiting only users with higher value of time is claimed to be the main factor that prevents implementation of such policies in practice. While many studies aimed to tackle the equity issues by certain welfare analyses, most of these studies fail to fully consider realistic features of users’ behavior and the uncertainty in link travel times. Given the variability of travel time in real-world networks and the impacts of pricing policies on path travel time distributions, it is important to consider the users’ reliability valuations, in addition to their travel time valuations. Thus, the goal in this study is to find an equitable pricing scheme that minimizes the total travel time of auto users in a general bimodal network considering heterogeneous users with different values of time and reliability. A particle swarm optimization algorithm is proposed to find self-funded and Pareto-improving optimal toll values. A reliability-based user equilibrium algorithm is embedded into this optimization algorithm to assign travelers to the equilibrated paths for different user classes given toll values. The proposed approach is successfully applied to a modified Sioux Falls network to explore impacts of subsidization, congestion level, and considering travel time reliability on the pricing strategy and its effectiveness.

Implementation of a New Travel Time Estimation Method for Demand Forecasting Models

2011 ◽  
Vol 130-134 ◽  
pp. 3410-3415
Author(s):  
Chen Xi Lu ◽  
Fang Zhao ◽  
Mohammed Hadi ◽  
Ren Fa Yang
Keyword(s):  
Travel Time ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Demand Forecasting ◽  
Estimation Method ◽  
Time Estimation ◽  
Signal Timing ◽  
Travel Time Estimation ◽  
Large Network ◽  
Demand Forecasting Model

This paper discusses the implementation of a new travel time estimation method in a regional demand forecasting model. The developed model considers implicitly the influence of signal timing as a function of main street and cross street traffic demands, although signal timing setting is not required as input. The application presented in this paper demonstrates that the developed model is applicable to a large network without the burden of signal timing input requirement. The results indicate that the application of the model can improve the performance of traffic assignment as part of the demand forecasting process. The model is promising to support dynamic traffic assignment (DTA) model applications in the future.

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

2021 ◽  
pp. 036119812110133
Author(s):  
Eun Hak Lee ◽  
Kyoungtae Kim ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim ◽  
Shin-Hyung Cho
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Public Transportation ◽  
Gradient Boosting ◽  
Log Data ◽  
Smart Card Data ◽  
Extreme Gradient Boosting ◽  
Express Train ◽  
Total Travel Time ◽  
The Individual

As the share of public transport increases, the express strategy of the urban railway is regarded as one of the solutions that allow the public transportation system to operate efficiently. It is crucial to express the urban railway’s express strategy to balance a passenger load between the two types of trains, that is, local and express trains. This research aims to estimate passengers’ preference between local and express trains based on a machine learning technique. Extreme gradient boosting (XGBoost) is trained to model express train preference using smart card and train log data. The passengers are categorized into four types according to their preference for the local and express trains. The smart card data and train log data of Metro Line 9 in Seoul are combined to generate the individual trip chain alternatives for each passenger. With the dataset, the train preference is estimated by XGBoost, and Shapley additive explanations (SHAP) is used to interpret and analyze the importance of individual features. The overall F1 score of the model is estimated to be 0.982. The results of feature analysis show that the total travel time of the local train feature is found to substantially affect the probability of express train preference with a 1.871 SHAP value. As a result, the probability of the express train preference increases with longer total travel time, shorter in-vehicle time, shorter waiting time, and few transfers on the passenger’s route. The model shows notable performance in accuracy and provided an understanding of the estimation results.

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